Resuscitation treatments for patients suffering from cardiac arrest generally include clearing and opening the patient's airway, providing rescue breathing for the patient, and applying chest compressions to provide blood flow to the victim's heart, brain and other vital organs. If the patient has a shockable heart rhythm, resuscitation also may include defibrillation therapy. The term basic life support (BLS) involves all the following elements: initial assessment; airway maintenance; expired air ventilation (rescue breathing); and chest compression. When all three (airway breathing, and circulation, including chest compressions) are combined, the term cardiopulmonary resuscitation (CPR) is used.
Current automated ECG rhythm analysis methods interrupt cardiopulmonary resuscitation (CPR) to avoid artifacts in the ECG resulting from chest compressions. Long interruptions of CPR have been shown to result in higher failure rate of resuscitation. Studies have reported that the discontinuation of precordial compression can significantly reduce the recovery rate of spontaneous circulation and the 24-hour survival rate. Y. Sato, M H. Weil, S. Sun, W. Tang, J. Xie, M. Noc, and J. Bisera, Adverse effects of interrupting precordial compression during cardiopulmonary resuscitation, Critical Care Medicine, Vol. 25(5), 733-736 (1997). Yu et al., 2002. Circulation, 106, 368-372 (2002), T. Eftestol, K. Sunde, and P A. Steen, Effects of Interrupting Precordial Compressions on the Calculated Probability of Defibrillation Success During Out-of-Hospital Cardiac Arrest, Circulation, 105, 2270-2273, (2002).
Management of breathing is another important aspect of the CPR process. Typical methods of monitoring breathing employ some form of impedance pneumography which measure and track changes in the transthoracic impedance of the patient. Currently, however, chest compressions result in significant artifact on the impedance signals, resulting in impedance-based pneumographic techniques as unreliable indicators of lung volume during chest compressions.
Adaptive filters have been attempted as a way of removing chest-compression artifacts in the ECG signal. S O. Aase, T. Eftestol, J H. Husoy, K. Sunde, and P A. Steen, CPR Artifact Removal from Human ECG Using Optimal Multichannel Filtering, IEEE Transactions on Biomedical Engineering, Vol. 47, 1440-1449, (2000). A. Langhelle, T. Eftestol, H. Myklebust, M. Eriksen, B T. Holten, P A. Steen, Reducing CPR Artifacts in Ventricular Fibrillation in Vitro. Resuscitation. March; 48(3):279-91 (2001). J H. Husoy, J. Eilevstjonn, T. Eftestol, S O. Aase, H Myklebust, and P A. Steen, Removal of Cardiopulmonary Resuscitation Artifacts from Human ECG Using an Efficient Matching Pursuit-Like Algorithm, IEEE Transactions on Biomedical Engineering, Vol 49, 1287-1298, (2002). H R. Halperin, and R D. Berger, CPR Chest Compression Monitor, U.S. Pat. No. 6,390,996 (2002). Aase et al. (2000) and Langhelle et al. (2001) used the compression depth and thorax impedance as reference signals for their adaptive filter. Husoy et al. (2002) extended this study by using a matching pursuit iteration to reduce the computational complexity; however, their results are usually computationally intensive, such as involving the calculation of a high order inverse filter. Halperin et al. (2002) proposed a frequency-domain approach using the auto- and the cross-spectrum of the signals and a time-domain approach using a recursive least square method for adaptive filtering the ECG signal. In both approaches, intensive computations are required.
There are numerous references available on adaptive filters. E.g., S. Haykin, Adaptive Filter Theory, Third Edition, Upper Saddle River, N.J., USA. Prentice-Hall, 1996